This site is about everything digital, giving an update on new things as I learn

Category: Measuring

Measure. Measure. Measure. Tracking the impact of a product is crucial if you wish to learn about your product and your customers. I’ve written before about the importance of spending time on defining the right metrics to measure, avoiding the risk of succumbing to data overload. That’s all well and good, but what do you do when the key things to measure aren’t so tangible!? For example, how do you measure customer feelings or opinions (a lot of which you’ll learn about during qualitative research)?

A few years ago, Kerry Rodden – whilst at Google – introduced the HEART framework which aims to solve the problem of measuring less tangible aspects of the products and experiences we create (see Fig. 1 below). The HEART framework consists of two parts:

The part that measures the quality of the user experience (the HEART framework)

The part that measures the goals of a project or product (the Goals-Signals-Metrics process)

HEART framework

Engagement – Measures the level of user involvement, typically via behavioural proxies such as frequency, intensity, or depth of interaction over some time period. Examples include the number of visits per user per week or the number of photos uploaded per user per day.

Adoption – New users of a product, feature or a service. For example: the number of accounts created in the last seven days, the number of people dropping off during the onboarding experience or the percentage of Gmail users who use labels.

Task success – This includes traditional behavioural metrics with respect to user experience, such as efficiency (e.g. time to complete a task), effectiveness (e.g. percent of tasks completed), and error rate. This category is most applicable to areas of your product that are very task-focused, such as search or an upload flow.

Does the product help achieve key customer tasks or outcomes? Why (not)?

What should we focus on? Why? How to best measure?

The HEART framework thus works well in measuring the quality of the user experience, making intangible things such as “happiness” and “engagement” more tangible.

Goals-Signals-Metrics process

The HEART framework goes hand in hand with the Goals-Signals-Metrics process, which measures the specific goals of a product. I came across a great example of the Goals-Signals-Metrics process, by Usabilla. This qualitative user research company applied the HEART framework and the Goals-Signals-Metrics when they launched a 2-step verification future for their users.

This example clearly shows how you can take ‘happiness’, a more intangible aspect of Usabilla’s authentication experience, and make it measurable:

Question: How to measure ‘happiness’ with respect to Usabilla’s authentication experience?

Goal: The overarching goal here is to ensure that Usabilla’s customers feel satisfied and secure whilst using Usabilla’s product.

Signals: Positive customer feedback on the feature – through a survey – is a strong signal that Usabilla’s happiness goal is being achieved.

Metrics: Measuring the percentage of Usabilla customers that feels satisfied and secure after using the new authentication experience.

The Usabilla example of the HEART framework clearly shows the underlying method of taking a fuzzy goal and breaking it down into something which can be measured more objectively.

Main learning point: The HEART framework is a useful tool when it comes to understanding and tracking the customer impact of your product. As with everything that you’re trying to measure, make sure you’re clear about what you’re looking to learn and how to best interpret the data. However, the fact that the HEART framework looks at aspects at ‘happiness’ and ‘engagement’ makes it a useful tool in my book!

Normally when I talk to other product managers about product pricing, I get slightly frightened looks in return. “Does that mean I need to set the price!?” or “am I now responsible for the commercial side of things too!?” are just some of the questions I’ve had thrown at me in the past.

“No” is the answer. I strongly believe that as product managers we run the risk of being all things to all people — see my previous post about “Product Janitors” — and I therefore believe that product people shouldn’t set prices. However, I do believe it’s critical for product people to think about pricing right from the beginning:

Do people want the product?

Why do they want it?

How much are they willing pay for it?

Answers to these questions will not only affect what product is built and how it’s built, but also how it will be launched and positioned within the market. I’ve made the mistake before of not getting involved in pricing at all or too late. As a result, I felt that I was playing catchup to fully understand the product’s value proposition and customers’ appetite for it.

Fortunately, there are two tools I’ve come across which I’ve found very helpful in terms of my comprehending the value a product is looking to achieve — both from a business and customer perspective: the Van Westendorp Pricing Sensitivity Meter and the Conjoint Analysis respectively.

At what price would you consider the product to be so expensive that you would not consider buying it? (Too expensive)

At what price would you consider the product to be priced so low that you would feel the quality couldn’t be very good? (Too cheap)

At what price would you consider the product starting to get expensive, so that it is not out of the question, but you would have to give some thought to buying it? (Expensive/High Side)

At what price would you consider the product to be a bargain — a great buy for the money? (Cheap/Good Value)

The aforementioned Van Westendorp questions are a good example of a so-called “direct pricing technique”, where the pricing research is underpinned by the assumption that people have a basic understanding of what a product is worth. In essence, this line of questioning comes down to asking “how much would you pay for this (product or service)?” Whilst this isn’t necessarily the best question to ask in a customer interview, it’s a nice and direct way to learn about how customers feel about pricing.

The insights from applying these direct questions will help in better understanding price points. The Van Westendorp method identifies four different price definitions:

Point of marginal cheapness (‘PMC’) — At the point of marginal cheapness, more sales volume would be lost than gained due to customers perceiving the product as a bargain and doubting its quality.

Point of marginal expensiveness (‘PME’) — This is a price point above which the product is deemed too expensive for the perceived value customers get from it.

Optimum price point (‘OPP’) — The price point at which the number of potential customers who view the product as either too expensive or too cheap is at a minimum. At this point, the number of persons who would possibly consider purchasing the product is at a maximum.

Indifference price point (‘IPP’) —Point at which the same percentage of customers feel that the product is getting too expensive as those who feel it is at a bargain price. This is the point at which most customers are indifferent to the price of a product.

Range of acceptable pricing (‘RAI’) — This range sits between the aforementioned points of marginal cheapness and marginal expensiveness. In other words, consumers are considered likely to pay a price within this range.

In addition to the Van Westendorp Price Sensitivity Meter, I’ve also used Conjoint Analysis to understand more about pricing. Unlike the Van Westendorp approach, the conjoint analysis is an indirect pricing technique which means that price is combined with other attributes such as size or brand. Consumers’ price sensitivity is then derived from the results of the analysis.

When designing a conjoint analysis study, the first step is take a product and break it down into its individual parts. For example, we could take a car and create combinations of its different parts to learn about combinations that customers prefer. For example:

Which of these cars would you prefer?

Option: 1

Brand: Volvo

Seats: 5

Price: £65,000

Option: 2

Brand: SsangYyong

Seats: 5

Price: £20,000

Option: 3

Brand: Toyota

Seats: 7

Price: £45,000

This is an overly simplified and totally fictitious example, but hopefully gives you a better idea of how a conjoint analysis takes into account multiple factors and will give you insight into how much consumers are willing to pay for a certain combination of features.

Main learning point: I personally don’t expect product managers to set prices for their products or design price research. However, I do think we as product managers benefits from a better understanding of the pricing model for our products and a better understanding of what constitutes ‘value for money’ for our customers. The Van Westendorp Price Sensitivity Meter and the Conjoint Analysis are just two ways of testing price sensitivity, but are in my view to good places to get started if you wish to get a better handle on pricing.

In my previous post I started looking at doing 5-day sprints to discover and test solutions for a problem that you’re trying to solve. This follows my reading of “Sprint” by Jake Knapp, John Zeratsky and Braden Kowitz. Once you’ve set the stage for a sprint, it’s time to kick things off: the first day of a sprint is all about agreeing on the challenge that you’re looking to have tackled by the end of the sprint. On the Monday, i.e. the first day of the sprint, the focus is on the following activities: (1) agreeing on a long-term goal (2) making a map of the challenge (3) asking experts and (4) picking a target.

Agree on a long-term goal (‘start at the end’)

You start the sprint by asking the the team “why”, make sure everyone is on the same page about what we’re trying to achieve. Why do we want to create this product? Why are we doing this project? Why do we want to solve this problem? Where do we want to be in 3 months, 6 months, 1 year, even 5 years from now and why? What will success look like? Agreeing on a long term will bring the answers together in a shared purpose.

Once you’ve got a shared understanding of the underlying “why” and have set a long-term goal, you come up with number of specific sprint questions, which you can derive from the assumptions and questions that the team might have. To get the team thinking about some of these questions, you can use the following prompts:

What questions do we want to ask in this sprint and why?

How will we subsequently utilise the answers to these sprint questions and outcomes?

To meet our long-term goal, what has to be true?

Imagine we travel into the future and our product or project failed. What might have caused that failure? How can we best mitigate this risk?

To reach customers for this product, what has to be true?

To deliver value to these customers, what has to be true?

Fig. 1 – Sample long term goal and sprint questions:

Long term goal: More people buying snacks online.

Sprint questions:

Are people looking to buy snacks online?

What is the experience customers are looking for when buying snacks online?

Map the challenge

Creating a map is a great way to understand the steps the customer has to go through to achieve a desired outcome (see a good example in Fig. 2 above). Each map is customer-centric, with a list of key actors on the left. Each map is a story, with a beginning, a middle and an end. These are the common elements of a Challenge Map:

List the actors (on the left) – The “actors” are all the important characters in your story. Most often, they’re different kinds of customers.

Write the ending (on the right) – Write the outcome that the customer wants to achieve.

Words and arrows in between – There’s no need for any fancy drawings; the map should be functional, and simple boxes and arrows should suffice.

Keep it simple – Your map should have from five to around fifteen steps. If there are more than twenty, your map is probably too complicated.

Ask for help – As you create the map, you should keep asking the team, “Does this map look right?” or “What are we missing?”

Ask the Experts

Nobody knows everything and it’s therefore critical that you engage with a range of ‘experts’. One of the biggest challenges of running a sprint is that you’ve got to gather a lot of information and make sense of it in a relatively short space of time. Having short conversations – approx. 30 minutes per conversation – with experts will help massively in collating relevant detail quickly.

Pick a Target

Selecting one target customer and one target event is the final activity of the first day of the sprint. The Decider needs to decide on the target customer and the customer event. Whatever she chooses will become the focus of the rest of the sprint – the sketches, prototype, and customer interviews all flow from this decision. Naturally, this can be a group decision, but it helps to assign decision-making responsibility to a single person.

Once you’ve selected a target, take a look back at your sprint questions. You usually can’t answer all those questions in one sprint, but one or more should line up with the target.

Main learning point: The first day of the sprint should really lay the groundwork for the rest of the sprint. Avoid the temptation to dive straight into solutions. Instead, spend the first day of the sprint to agree on a long-term goal and selecting a specific target to focus on!

As part of my product management toolkit, I’ve thus far covered the creation of a product vision and the definition of a product strategy. The next thing to look at is goal setting: what are the business goals that a product strategy and or roadmap need to align with? I’ve learned the importance of goals to help define or assess a product strategy. I would even go as far as saying that if your product strategy, roadmap, backlog or – low and behold – your actual product don’t align with your business goals, you’re setting yourself up for failure.

Tool 3 – Goal Setting

What are goals? – This is what Wikipedia has to say about “goals”: “A goal is a desired result that a person or a system envisions, plans and commits to achieve; a personal or organizational desired end-point in some sort of assumed development. Many people endeavour to reach goals within a finite time by setting deadlines.” In other words, what is it the that we are looking to achieve, why and by when?

I typically look at goals from either of the following two angles: metrics or ‘objectives-key-results’ (‘OKR’s). From a metric perspective; what is the single metric that we’re looking to move the needle on, why and and by when? What does this impact look like and how can we measure it? For example, a key business goal can be to increase Customer Lifetime Value with 1% by June 2016. To be clear, a metric in itself isn’t a goal, the change that you want to see in metric is a goal.

From an OKR perspective, the idea is to outline a number of tangible results against a set, high level, objective. For example:

Objective: To enable sellers on our marketplace platform to make business and product decisions based on their sales and performance data generated from their activities on our platform.

Result 1: Our sellers making key business and product decisions before and throughout Christmas 2015

Result 2: Our sellers can look at their historic sales data so that they’ve got more sales context for their decision-making

Typically, there will be a set of overarching business goals that have been established and our responsibility as product managers is to link our product goals to these objectives, so that our product strategy is fully aligned with the business strategy.

What goals aren’t – Goals aren’t a strategy or specific features. This might sound obvious, but often see cases where people do confuse things; setting goals without a strategy to achieve them or having a roadmap that doesn’t align with business goals.

In contrast, the point of a strategy or a roadmap is to highlight the ‘how’, the steps that need to be taken to achieve specific goals.

When to create goals? – It’s simple: if you join an organisation and hear “we don’t have business goals”, you know what to do! My point here is that a product strategy or roadmap that isn’t aligned with broader business goals, is just a loose collection of features or random solutions. The one thing to add is that some early stage startups tend to get really hung up on a whole range of specific goals or metrics. I’d always recommend to keep it simple and focus on a single goal or metric, understand what your (target) users’ needs are and how are they actually using your product.

Characteristics of good goals – I can imagine that a lot of you will have a heard of SMART goals:

Main learning point: In my view, setting and understanding goals is just important as creating a strategy to achieve them. Before I delve into creating a product strategy or roadmap, I’ll always try to make sure I fully understand the business objectives and translate those into specific, measurable product goals.

You might have read my one of my previous blog posts about the so-called goal oriented roadmap. I prefer goal-oriented roadmaps over their more traditional counterparts. My problem is that ‘classic’ roadmaps contain a mix of features and timings, but don’t provide any context whatsoever (I’ve included an example in Fig. 1 below). I typically don’t include features on a roadmap and focus on user or business problems instead. As such, the roadmap becomes a tool for ‘working backwards’ – enabling product teams to explore solutions for given problems or desired results.

I recently learnt from Jared Spool and Bruce McCarthy about adding “themes” to product roadmaps. McCarthy told Spool about the concept of adding themes, and Spool became an instant fan. Like me, McCarthy isn’t a fan of having features on a roadmap. Instead, he suggests adding “themes” to the roadmap and this is why:

Open to ‘options’ – McCarthy believes that by having specific features on a roadmap, you run the risk of sending a product team down a certain avenue (and closing off any potential side streets). For example, if you were to just put “data dashboard” on a roadmap, chances are you’ll end up with a data dashboard. However, what would happen if you were to put “provide our customers with data to make key decisions” or “no data access” on the roadmap instead? By focusing solely on solution you run the risk of falling victim to what Josh Wexler calls solution sickness. Solution sickness is all about fixating on a solution and ignore any alternative ways of solving a problem.

Customer focus – Jared Spool makes a great point when he says “When companies talk about features, they are saying, “Look at us. Look at what we can do.” He goes on to explain that “When companies talk about the problems of customers, they are saying, “Look at what you’re dealing with. Look at how we want to help.” It’s very easy to get fixated on features and forget about the underlying problems you’re looking to solve.

Helping trade-off decisions – One of the things I love about both the goal-oriented and ‘theme’ approach to roadmapping is that it forces you to consider ‘why’ you want to develop certain products or services. What problem are we looking to solve and why? Why do we feel this problem is worth solving? Why should we prioritise this problem over others? Both the goal oriented and the theme roadmap approaches help make customers and their problems the core of everything you do as a product person. When I use the goal oriented roadmap, I always take out the “feature” layer purely because I don’t want us to be pinned down to one solution.

“Marketing the story of solutions” – Which do you think would be an easier story to sell to customers? Option 1: “You can now use this real-time data dashboard which you can access via your content management system, enabling you to filter the data and extract reports” or Option 2: “You can use our data to decide on whether now is good time to sell your house?” … Easy, isn’t it!? As Jared Spool points out, the ‘theme’ approach makes it much easier for marketing teams to tell a story about a product or service: “Here are the problems we set out to solve and here’s how we solved them.”

A more cohesive design – Design is another area which is positively impacted by a shift in focus from solutions to problems. From experience, I know how much easier it is to prioritise against a problem that you’re looking to solve, instead of trying to cram in a lot of features which you think might have an impact. As Spool puts it: ” Without a commitment to specific solutions, the team has flexibility.” As a result, product teams stand a better chance of creating simpler, well designed products.

Main learning point: I really encourage all product managers – the ones who aren’t doing so already – to think much more in terms of user problems instead of focusing on features. Not only will this help you in focusing your product efforts, I believe it will also make you much more customer centric.

Since I started looking into omni-channel metrics last year, I’ve been learning how to best gather meaningful data at each step of the user journey. I recently came across a great piece by Gary Angel titled “A Data Model for the User Journey”. In his article, Gary aims to address the multi-source nature of our data touchpoints, and the issues brought about by the differences in the level and type of detail data. He rightly points out that these differences in data make any kind of meaningful analysis of the user journey virtually impossible. Gary provides a number of useful steps to tackle this problem:

Create a level of abstraction – Gary first suggestion is to get to a level of abstraction where each data touchpoint can be represented equally. One way of doing this is to apply Gary’s “2-tiered segmentation” model. In a 2-tiered segmentation model, the first tier is the visitor type. This is the traditional visitor segmentation based on persona or relationship. The second tier is a visit or unit-of-work based segmentation that is behavioural and is designed to capture the visit intent. It changes with each new touch. Gary summarises this two-tiered approach as follows: “Describing who somebody is (tier 1) and what they are trying to accomplish (tier 2).”

Capture visit intent – One of the key things that I learned from Gary’s article is the significance of ‘visit intent’ with respect to creating a user-journey model. Visit intent offers an aggregated view of what a visit was about and how successful it was. Both the goal and the success of a visit are important items when analysing a user journey.

2-tiered segmentation and omni-channel – Gary points out how well his 2-tiered segmentation model lends itself to an omni-channel setup. The idea of 2-tiered segments can be used across any touchpoint, whether it’s online or offline. The intent-based segmentation can be applied relatively easily to calls, branch or store visits and social media posts. The model can also be applied – albeit less easily – to display advertising and email (see Fig. 1 below).

Good starting point for journey analysis – When you look at the sample data structure as outlined in Fig. 1 below, with one data row per user touchpoint visit or unit of work, you can start doing interesting pieces of further analysis. For example, with this abstract data structure you can analyse multi-channel paths or enhance user journey personalisation.

Combine visitor level data with user journey data – It sounds quite complex, but I like Gary’s suggestion to model in the abstract the key customer journeys. This can then be used to create a visitor level data structure in which the individual touchpoints are rolled up. Gary’s example below helps clarify how you can best map different data touchpoints to related stages in the user journey (see Fig. 2 below) .

Main learning point: The main thing that I’m taking away from Gary Angel’s great piece is the two segments to focus on when measuring the user journey: the visitor and their goals. The data structure suggested by Gary lends itself really well to an omni-channel user experience as it combines visitor and user journey data really well.

With an abstract model like this in hand, you can map your touchpoint types to these stages in user journey and capture a user-journey at the visitor level in a data structure that looks something like this:

VisitorID

Journey Sub-structure

Journey Type (Acquisition)

Current Stage (Feature Narrowing)

Started Journey On (Initial Date)

Time in Current Stage (Elapsed)

Last Touch Channel in this Stage (Channel Type – e.g. Web)

Last Touch Success

Last Touch Value

Stage History Sub-Structure

Stage (e.g. Initial Research) Start

Stage Elapsed

Stage Success

Stage Started In Channel

Stage Completed in Channel

Channel Usage Sub-Structure

Web Channel Used for this Journey Recency

Web Channel Used for this Journey Frequency

Call Channel Used for this Journey Recency

Call Channel Used for this journey Frequency

Etc.

Stage Value

Etc.

This stage mapping structure is a really intuitive representation of a visitor’s journey. It’s powerful for personalisation, targeting and for statistical analysis of journey optimisation. With a structure like this, think how easy it would be to answer these sorts of questions:

Which channel does this visitor like to do [Initial Product Research] in?

How often do visitors do comparison shopping before brand narrowing?

When people have done brand narrowing, can they be re-interested in a brand later?

I’m still doing my Stanford online course on relational databases. Today, I learned about the basics of SQL, a special programming language designed for managing data held in a relational database or from stream processing in a .

The teacher of the class, Jennifer Widom, kicked off the class by talking about the difference between a Data Definition Language (‘DDL’) and a Data Manipulation Language (‘DML’):

Data Definition Language (‘DDL’)

Create a table in the database

Drop a table from the database

Data Manipulation Language (‘DML’)

Query the database -> “Select” statement

Modify the database -> “Insert”, “Alert” or “Update” statement

Jennifer then told us about the Basic “Select” statement (see Fig. 1 below), explaining that the result of such a statement is to return a relation with a set of data attributes. For example, when you take a simple college admissions database as a starting point where there are 3 relations, each relation having its own set of unique attributes:

College ( College Name, State and Enrollment)

Student (Student ID, Student Name, GPA and Size High School)

Apply (Student ID, College Name, Major and Decisions)

Jennifer then gave us the following examples:

Query involving a single relation

select sID, sName, GPA

from Student

where > 3.6

This query will give you the name and student IDs of those applicants with a GPA higher than 3.6.

Query combining two relations

select sName, Major

from Student, Apply

where Student.sID = Apply.sID

This query will give you data on the names and student IDs for those students applied, filtered by Major.

Jennifer pointed out that SQL is a multi-set model and it therefore allows duplicates. You can eliminate duplicate values by adding the keyword “distinct” to your query. Jennifer also mentioned that SQL is an unordered model which means that you can sort results.

You can include an “order by” clause in your query and add “descending” to order the results of your query:

where Apply.sID = Student.sID

and Apply.cName = College.cName

order by GPA desc, Enrollment;

Main learning point: I found this class about creating a basic “select” statement particularly helpful, as it helped me to get a better understanding of how basic SQL queries are constructed.